Cross-setting replication of the associations between maternal health and autism

Cross-setting replication of the associations between maternal health and autism | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Cross-setting replication of the associations between maternal health and autism View ORCID Profile Vahe Khachadourian , Meredith Anderson , Elias Speleman Arildskov , Jakob Grove , Abraham Reichenberg , Sven Sandin , Diana Schendel , Stefan Nygaard Hansen , Lisa A Croen , View ORCID Profile Magdalena Janecka doi: https://doi.org/10.1101/2025.06.12.25329531 Vahe Khachadourian 1 Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine , New York, NY, US Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Vahe Khachadourian Meredith Anderson 2 Division of Research, Kaiser Permanente Northern California , Pleasanton, CA, US Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elias Speleman Arildskov 3 Department of Biomedicine, Aarhus University , Aarhus, DK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jakob Grove 3 Department of Biomedicine, Aarhus University , Aarhus, DK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abraham Reichenberg 4 Department of Psychiatry, Icahn School of Medicine at Mount Sinai , New York, NY, US 5 Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai , New York, NY, US Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sven Sandin 4 Department of Psychiatry, Icahn School of Medicine at Mount Sinai , New York, NY, US 6 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Diana Schendel 7 A.J. Drexel Autism Institute, Drexel University , Philadelphia, PA, USA 8 The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH , Aarhus, Denmark 9 National Centre for Register-Based Research, Aarhus BSS, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stefan Nygaard Hansen 11 Department of Population Health, NYU Grossman School of Medicine , New York, NY, US Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lisa A Croen 2 Division of Research, Kaiser Permanente Northern California , Pleasanton, CA, US Find this author on Google Scholar Find this author on PubMed Search for this author on this site Magdalena Janecka 1 Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine , New York, NY, US 11 Department of Population Health, NYU Grossman School of Medicine , New York, NY, US Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Magdalena Janecka For correspondence: magdalena.janecka{at}nyulangone.org Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background Autism spectrum disorder (ASD) is a neurodevelopmental condition with early-life origins. Maternal health conditions during pregnancy have been linked to autism risk, but most studies focus on single populations, limiting generalizability. In this study we examined whether associations previously reported in a Danish registry-based study hold in a U.S. cohort. Methods We analyzed electronic health records of children born between 2010 and 2017 at Kaiser Permanente Northern California (KPNC) and their mothers. Maternal diagnoses were classified as chronic or non-chronic and their potential associations with ASD diagnosis in the child were assessed using Cox proportional hazards models, adjusting for key sociodemographic, healthcare utilization, and comorbid maternal diagnoses. Methods were aligned with the Danish study to ensure comparability. Results Among 224,353 children in the KPNC cohort, 5,448 (2.4%) were diagnosed with autism. Of the 42 maternal diagnoses significantly associated with autism in Denmark, 38 were evaluable in KPNC, and 18 remained statistically significant after full adjustment. The majority of associations had point estimates consistent with the Danish study, particularly psychiatric and cardiometabolic conditions (e.g., bipolar disorder: HR = 1.62, 95% CI = 1.30–2.02; gestational diabetes: HR = 1.22, 95% CI = 1.12–1.32). Conclusions Despite demographic and healthcare differences, and some differences in effect magnitudes of maternal health-autism associations, 35 of the 38 associations found in the Danish study replicated, qualitatively (direction of the effect), in the U.S. cohort, suggesting robust cross-setting relevance. Further research is needed to explore underlying mechanisms and potential effect modifiers. INTRODUCTION Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by differences in social communication and restrictive, repetitive behaviors, often detected in early childhood. For brevity, throughout the article, we use ‘autism’ to refer to a diagnosis of autism spectrum disorder. Given the early-life origins of autism, and the extensive maternal-fetal interface that is established during pregnancy, etiological research has focused on perinatal influences as potential risk factors for autism. Epidemiological studies have identified links between maternal health during pregnancy — including diagnoses of depression 1 , diabetes 2 , immune system disorders 3 , and infections 4 — and an increased risk of autism in offspring. However, many maternal health conditions have been explored in relation to autism risk individually, often with differing methodologies across the studies. This precludes comparisons of autism risk across different maternal conditions within a study setting, as well as drawing robust conclusions about the extent to which these associations replicate between different settings. Addressing the historically limited scope of within-setting comparisons, our recent registry-based study in Denmark 5 evaluated a broad spectrum of maternal diagnoses during pregnancy in the context of autism risk in offspring. Of the 236 maternal diagnoses evaluated in that study, 42 were statistically significantly associated with autism in the offspring. While this enabled us to compare the strength of the different associations within the same setting, findings were based on a population that is relatively homogeneous in terms of genetic ancestry, and access to care (Danish). Although Denmark, like other Nordic countries, provides an unparalleled opportunity to identify autism risk factors – due to the population-wide coverage of the health registers, reliable longitudinal person tracking, and universal healthcare – the generalizability of the findings to cohorts with more diverse health care access and demographics, remains unknown. The extent to which the effects of perinatal risk factors for autism replicate between settings is not well understood, limiting the interpretation of observational associations in a single setting, and reducing the portability of this information for clinical prediction in different settings. Therefore, the current study aims to examine the generalizability of the associations between maternal health conditions during pregnancy and the risk of autism in offspring characterized in the Danish National Register. To this end, we examined the replication of the associations as indicated by the direction and statistical significance of the reported effects - reported in Denmark in a pregnancy cohort with Kaiser Permanente Northern California (KPNC) membership. KPNC has a large membership with diverse sociodemographic characteristics and comprehensive maternal and child health records, enabling close alignment of the analytical methods with those implemented in the Danish study. METHODS The study population included children born within KPNC between January 1, 2010 and December 31, 2017. KPNC is a large, integrated healthcare system that serves over 4.5 million members. The sociodemographic profile of KPNC members closely reflects the local and statewide population of California with the exception of individuals at the highest and lowest ends of the income spectrum who are underrepresented 6 , 7 . Children without KPNC enrollment after birth, whose mothers were younger than 13 years or older than 55 years at the time of the child’s birth, or who had missing data on covariates were excluded. Children were also excluded if their mothers did not have KPNC membership for at least 3 months during each year in the 2 years preceding delivery. Children were followed until 1) February 29, 2020, 2) first ASD diagnosis after 1 year of age, 3) disenrollment from KPNC, or 4) death, whichever came first. Exposure Exposures were maternal diagnoses around pregnancy ( Table S1 ), identified using the electronic health records (EHRs) from both inpatient and outpatient settings. We used the Chronic Condition Indicator developed by the Agency for Healthcare Research and Quality 8 to distinguish between chronic and non-chronic diagnoses. For non-chronic diagnoses, the detection period was the 12 months before childbirth, whereas for chronic diagnoses, the detection period was 48 months before childbirth. The longer detection period for chronic diagnoses reflects the assumption that these conditions are permanent, and their onset cannot be precisely dated using EHR data. A wider detection window enhances sensitivity for capturing maternal chronic conditions that might not be recorded near pregnancy (e.g., well-managed conditions requiring minimal care) but could still impact the fetus. A series of sensitivity analyses assessing the impact of using alternative detection periods is provided elsewhere 5 . Given the study objective, exposures were limited to the 42 diagnoses found to be significantly associated with autism in the study in the Danish population. Outcome Outcome was defined as the first autism diagnosis after 12 months of age recorded in the child EHR and based on the DSM criteria applicable to ASD diagnosis at the time of evaluation (DSM-IV or DSM-5) 9 . Among children in the cohort with autism, 85% were evaluated at a KPNC ASD evaluation center, where a multidisciplinary team employed a standardized protocol, including the Autism Diagnostic Observation Schedule 10 . The remaining 15% received diagnoses from child psychiatrists, developmental-behavioral pediatricians, pediatric neurologists, or general pediatricians. Covariates Covariates included maternal age at delivery, insurance type at delivery (categorized as KPNC insurance vs. Government insurance), race/ethnicity (Asian/Pacific Islander, Black, Hispanic, White, and other/unknown), child’s birth year, and sex, all obtained from KPNC EHRs. Insurance type served as a proxy for socioeconomic status; note this is in contrast to the Danish study, where the SES proxies included maternal income and education, not available in the KPNC records. Additionally, the number of days the mother had any in- or outpatient healthcare encounter in the year preceding the child’s birth provided a proxy for healthcare utilization (including both primary care and specialist encounters; in the Danish study, primary care information was limited to midwife-reported diagnoses during pregnancy). Statistical Analysis We assessed the association of each maternal diagnosis with autism separately in a series of Cox proportional hazard models, incrementally adjusting for the study covariates. The association between each maternal diagnosis and autism in offspring was analyzed adjusting first for child’s sex and birth year, then adding maternal age at delivery, then adding insurance type, and finally adding number of healthcare encounters the mother had during the 12 months preceding the child’s birth. We used clustering sandwich estimators to account for potential within-family correlation due to the presence of siblings in the dataset. All maternal diagnoses occurring in <5 mothers of children with autism and/or mothers of children without autism were excluded from the analyses to minimize the risk of sparse data bias 11 . Finally, to adjust for possible comorbidity between diagnoses, all diagnoses evaluated individually were concurrently included in a multi-diagnosis model, adjusting for all covariates. All tests of statistical hypotheses were conducted on the 2-sided 5% level of significance. All statistical programs were aligned with the original study in the Danish birth cohort 5 . RESULTS We identified 227,195 children born at KPNC from January 1, 2010 to December 31, 2017. After excluding one child (<0.01%, no autism) with unknown sex and 2,841 children (1.2% overall, 59 with autism (1.1%) and 2,782 without autism (1.2%)) whose insurance type at delivery was unknown, the study sample included 224,353 children. The median duration of follow-up was 3.7 years (IQR: 1.3 – 6.4 years). A total of 5,448 (2.4%) children received an ASD diagnosis during the study follow-up period. The median age at diagnosis was 3.0 years (IQR: 2.2 - 4.0 years). The median maternal age at the time of delivery was 31.9 years (IQR: 28.3 – 35.3 years). White children were the largest racial/ethnic group (41.3%) in the sample, followed by Asian/Pacific Islander (26.3%) and Hispanic (23.4%) children. Table S2 presents the sample characteristics of covariate data by ASD diagnosis status. Maternal diagnoses and offspring autism Of the 42 maternal diagnoses significantly associated with autism in Denmark, 4 diagnoses were observed in fewer than five mothers of children with autism in the KPNC cohort, corresponding to a prevalence of less than 0.1% per diagnosis in KPNC. After excluding these rare diagnoses whose effects could not be reliably estimated in the KPNC sample, 38 diagnoses were evaluated for their association with offspring autism. Prevalence of several of these diagnoses differed substantially in the Danish and Californian cohorts, including over 10-fold differences in postpartum hemorrhage (7.4% in DK vs 0.2% in KPNC) or abnormality of the forces of labor (1% in DK vs. 10.5% in KPNC). Table S3 provides the number of exposed children by autism status. In the fully adjusted model ( Figure 1 and Table S4 ), 18 diagnoses were statistically significantly associated with autism, all of which with point estimates >1. Apart from multiple gestation, all statistically significant diagnoses had point estimates consistent with those observed in the Danish cohort study (multiple gestation, KPNC: HR = 1.25, 95% CI = 1.11–1.40; DK: HR = 0.85, 95% CI = 0.78-0.93). Maternal diagnoses with a consistent association with autism in the KPNC setting included psychiatric (e.g., bipolar disorder; KPNC: HR = 1.62, 95% = 1.30–2.02; DK: HR = 2.14, 95% CI = 1.43-3.20) and cardiometabolic conditions (e.g., diabetes mellitus in pregnancy; KPNC: HR = 1.22, 95% CI = 1.12–1.32, DK: HR = 1.23, 95% CI = 1.12-1.36; primary hypertension; KPNC: HR = 1.20, 95% = 1.06–1.36; DK: HR = 1.34, 95% CI = 1.08-1.67). Download figure Open in new tab Figure 1. Associations between ICD-10 level 3 maternal diagnoses and offspring autism from single- and multiple-diagnosis models in KPNC and Danish cohorts All estimates and their confidence interval are from the fully adjusted model accounting for maternal age at delivery, child’s birth year, sex and the measures of SES (insurance type at delivery; race/ethnicity; number of days the mother had any healthcare encounter in the year preceding the child’s birth); NB. In Denmark, the measures of SES included instead maternal income and education, not available in the KPNC records. Of the 20 diagnoses that were significantly associated with autism in the Danish sample but did not reach statistical significance in the KPNC sample, 16 had point estimates that were qualitatively consistent with those in the Danish study (HR > 1 or HR < 1). For the 4 diagnoses with point estimates in the opposite direction in the Danish and KPNC samples, confidence intervals of the estimated effect were overlapping for all but one diagnosis (Other systemic involvement of connective tissue, DK: HR = 1.68, 95%CI =1.32–2.15; KPNC: HR = 0.59, 95%CI =0.32–1.10; NB. this diagnosis was very rare in KPNC, with only 10 diagnoses in pregnancies leading to an ASD diagnosis in the child). Figure 1 provides a comprehensive comparison of the results from the KPNC and Danish cohorts in the fully adjusted models; a comparison of the intermediate models is presented in Table S4 . Finally, in the multi-diagnosis model, which adjusted for covariates and all 38 maternal diagnoses identified in the fully adjusted single-diagnosis models, we observed consistency of the results across KPNC and Denmark, similar to those observed in the single-diagnosis models. A summary of all 38 associations in the multi-diagnosis models and the respective covariate coefficients are presented in Figure 1 and Table S5 . DISCUSSION We tested the replication of the associations between maternal diagnoses around pregnancy and off-spring autism in Denmark and an EHR-based cohort in Northern California, encompassing over 200,000 pregnancies with diverse sociodemographic characteristics. Despite the substantial differences in sociodemographic characteristics and study settings, we observed the same direction of the effect in 35 out of 38 of the associations observed in the Danish study. These results suggest the generalizability of the associations between maternal health and child autism beyond the Danish population. Our findings from the Danish study supported previously reported associations, e.g., between maternal obesity or obsessive-compulsive disorder and autism. Here, we further extend their relevance by demonstrating generalizability to a population beyond Denmark. While other studies have previously indicated the generalizability of certain associations – for example, the association between gestational diabetes and autism has been reported in 2 and 12 – our study is the first to perform such replication in a fully systematic manner by closely aligning the study methods across the settings. The associations between maternal diagnoses and autism replicated despite the differences in the demographics, health profiles, and healthcare systems between the U.S. and Denmark ( Table S6 ). For example, the prevalence of multiple maternal diagnoses differed substantially between the two studies, which could be attributable to differences in the health profiles, healthcare systems, and surveillance and diagnostic coding practices 13 , 14 . While ‘perfect’ cross-setting replication is rarely feasible in epidemiological studies, our results indicate that the reported associations are unlikely to arise due to the factors that differ across the settings. Importantly, replication of the study findings does not necessarily provide evidence for causality of maternal health in autism etiology, as the replicated associations could be attributable to non-causal mechanisms in both cohorts. In fact, the study in Denmark demonstrated that familial confounding was likely key in most of the observed associations between maternal diagnoses and offspring autism. Results from KPNC suggest that these familial factors may overlap across the settings (as would be the case for genetic factors, for example), however, it remains possible that the potential confounders contributing to the associations differ across the settings. Our study represents the first systematic and comprehensive replication of the associations between maternal health and autism across diverse settings. While in both settings we used large, population-based samples, our approach has several limitations. First, while the associations were largely consistent and robust despite differences in diagnosis prevalence, healthcare systems, population characteristics and length of the follow-up period, there were numerical differences in the magnitude of some of the associations. For the majority (35) of the 38 diagnoses evaluated in this study, the estimated effects had wide confidence intervals, making it difficult to determine whether the observed differences in the estimated associations are due to a true heterogeneity of the effects in different populations and settings or simply reflect statistical uncertainty in the estimates. Secondly, while the Danish study relied on a national registry, the results from KPNC may not generalize across the US; although the cohort includes individuals from a wide range of racial, ethnic, and socioeconomic backgrounds, mirroring the diversity in the U.S., it may underrepresent the 11% of U.S. population, between ages 18-64, that are uninsured 15 . In conclusion, our results demonstrated a replication of multiple associations between maternal diagnoses and offspring autism across populations and settings. These results indicate the potential cross-setting relevance of the study results and provide early evidence that these associations are attributable to factors that are beyond differences in cohort characteristics between the settings (e.g., demographic composition). Further studies are needed to characterize broader generalizability of these associations, assess potential effect measure modification (e.g., by maternal age), and identify factors contributing to potential effect heterogeneity across different settings. Data Availability We share summary statistics of all association tests performed in the study. Based on the ethical approvals, we are not allowed to share individual patient data. Analytical code will be made available to others, enabling replication by individuals with an approved access to the data resources used in the study. TABLES View this table: View inline View popup Download powerpoint Table S1. View this table: View inline View popup Download powerpoint Table S2. Demographic characteristics of the analytical sample (mother-child dyads) View this table: View inline View popup Download powerpoint Table S3. Frequency of maternal diagnosis by autism status in KPNC and Danish cohorts View this table: View inline View popup Download powerpoint Table S4. Associations between ICD-10 level 3 maternal diagnoses and offspring autism from single-diagnosis models in KPNC and Danish cohorts View this table: View inline View popup Download powerpoint Table S5. Associations between ICD-10 level 3 maternal diagnoses and offspring autism from multi-diagnosis models in KPNC and Danish cohorts View this table: View inline View popup Download powerpoint Table S6. Demographic characteristics of the analytical sample (mother-child dyads) REFERENCES 1. ↵ Hagberg KW , Robijn AL , Jick S. Maternal depression and antidepressant use during pregnancy and the risk of autism spectrum disorder in offspring . Clin Epidemiol . 2018 ; 10 : 1599 – 1612 . doi: 10.2147/CLEP.S180618 OpenUrl CrossRef PubMed 2. ↵ Xiang AH , Wang X , Martinez MP , et al. Association of maternal diabetes with autism in offspring . 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Int J Med Inform . 2008 ; 77 ( 1 ): 50 – 57 . doi: 10.1016/j.ijmedinf.2006.11.005 OpenUrl CrossRef PubMed Web of Science 15. ↵ Cohen RC , Amy E. Health insurance coverage: Early release of estimates from the National Health Interview Survey , 2022. National Center for Health Statistics . May 2023 . Published online 2023. View the discussion thread. Back to top Previous Next Posted June 13, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Cross-setting replication of the associations between maternal health and autism Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. 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Share Cross-setting replication of the associations between maternal health and autism Vahe Khachadourian , Meredith Anderson , Elias Speleman Arildskov , Jakob Grove , Abraham Reichenberg , Sven Sandin , Diana Schendel , Stefan Nygaard Hansen , Lisa A Croen , Magdalena Janecka medRxiv 2025.06.12.25329531; doi: https://doi.org/10.1101/2025.06.12.25329531 Share This Article: Copy Citation Tools Cross-setting replication of the associations between maternal health and autism Vahe Khachadourian , Meredith Anderson , Elias Speleman Arildskov , Jakob Grove , Abraham Reichenberg , Sven Sandin , Diana Schendel , Stefan Nygaard Hansen , Lisa A Croen , Magdalena Janecka medRxiv 2025.06.12.25329531; doi: https://doi.org/10.1101/2025.06.12.25329531 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Epidemiology Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4435) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15228) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6599) Geriatric Medicine (668) Health Economics (997) Health Informatics (4536) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3332) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9231) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a006c35d6ec24807',t:'MTc3OTU2NzY0Ng=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();